Pandas (i.e. panel data) is a Python library designed to manipulate data in tables and time series. Pandas uses many Numpy library functions to manipulate data stored in dataframes, analogous to a spreadsheet or table. Let’s look at some basic Pandas functions to manipulate data, and plot the data using the Seaborn plotting package. To begin, import libraries and simulate
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In programming, for loops are used to automate repetitive tasks, such as analysing similar datasets from different subjects. Sometimes, a dataset might be slightly different to another such that the data can be used but needs to be analysed differently in the loop. For example, data might have been accidentally sampled at a higher rate, or a nested trial needs
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Spike2 is a piece of software written by the folks at CED (Cambridge Electronic Design) to collect data with their data acquisition boards. As pointed out in a previous post, Spike2 can be used to analyse data. However, the drop-down menus and options make it difficult to ensure processing is consistent from session to session. Spike2 comes with its own
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Suppose you have some big data files to analyse, and you would like to know roughly how long it takes to run the analysis. This is so that in future, if you had to re-run the analysis, you could gauge that it will take long enough to go get a coffee or work on another job while you wait for
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Scientific figures are at their most informative when they include the individual data used to calculate summary statistics such as means and standard deviations. Why is showing data important? As previously pointed out here and here, figures with means, standard deviations, standard errors, etc. can be misleading and conceal the nature of the underlying data. As highlighted in our previous
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Jupyter notebooks are a scientific computing development platform that can run many different programming languages, including Python via the interactive Python interpreter IPython. The key advantage in a notebook environment is that code can be sectioned into cells for testing while retaining all the interactive features of IPython, including magic commands. IPython magics are a specialised set of commands hosted
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